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from diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image |
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from diffusers.utils import load_image |
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import torch |
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from pathlib import Path |
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import os |
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from huggingface_hub import HfApi |
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torch.backends.cuda.matmul.allow_tf32 = True |
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torch_device = "cuda" if torch.cuda.is_available() else "cpu" |
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api = HfApi() |
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pipe = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
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pipe = pipe.to(torch_device) |
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pipe.enable_xformers_memory_efficient_attention() |
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img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" |
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mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" |
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image = load_image(img_url) |
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mask_image = load_image(mask_url) |
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prompt = "dslr photography of an empty bench, high quality" |
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generator = torch.Generator(device="cuda").manual_seed(0) |
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image = pipe( |
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prompt=prompt, |
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image=image, |
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mask_image=mask_image, |
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guidance_scale=8.0, |
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num_inference_steps=20, |
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generator=generator, |
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).images[0] |
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image = image.resize((1024, 1024)) |
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pipe = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
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pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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image = pipe( |
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prompt=prompt, |
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image=image, |
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mask_image=mask_image, |
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guidance_scale=8.0, |
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num_inference_steps=100, |
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strength=0.2, |
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generator=generator, |
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).images[0] |
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pipe = AutoPipelineForImage2Image.from_pipe(pipe) |
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pipe.enable_xformers_memory_efficient_attention() |
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image = pipe( |
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prompt=prompt, |
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image=image, |
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guidance_scale=8.0, |
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num_inference_steps=100, |
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strength=0.2, |
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generator=generator, |
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).images[0] |
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file_name = f"aaa" |
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path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.png") |
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image.save(path) |
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api.upload_file( |
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path_or_fileobj=path, |
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path_in_repo=path.split("/")[-1], |
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repo_id="patrickvonplaten/images", |
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repo_type="dataset", |
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) |
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print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png") |
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